Occupancy detection of an office room from light, temperature, humidity and CO2 measurements. Created by university of Nottingham 3.1 Synthetic objects The YOLOv5 labeling algorithm proved to be very robust towards the rejection of pets. Dark images (not included in the dataset), account for 1940% of images captured, depending on the home. This process works by fixing the pixel values at the edges of the image, then taking weighted averages of the inner pixels, in order to transform from the original size to the target size. 2 for home layouts with sensor hub locations marked. 0-No chances of room occupancy Inspiration ), mobility sensors (i.e., passive infrared (PIR) sensors collecting mobility data) smart meters (i.e., energy consumption footprints) or cameras (i.e., visual WebGain hands-on experience with drone data and modern analytical software needed to assess habitat changes, count animal populations, study animal health and behavior, and assess ecosystem relationships. Instead, they have been spot-checked and metrics for the accuracy of these labels are provided. Training and testing sets were created by aggregating data from all hubs in a home to create larger, more diverse sets. The homes with pets had high occupancy rates, which could be due to pet owners needing to be home more often, but is likely just a coincidence. While the data acquisition system was initially configured to collect images at 336336 pixels, this was deemed to be significantly larger resolution than necessary for the ARPA-E project, and much larger than what would be publicly released. The number of sensor hubs deployed in a home varied from four to six, depending on the size of the living space. 5 for a visual of the audio processing steps performed. Figure8 gives two examples of correctly labeled images containing a cat. Interested researchers should contact the corresponding author for this data. Are you sure you want to create this branch? government site. WebCNRPark+EXT is a dataset for visual occupancy detection of parking lots of roughly 150,000 labeled images (patches) of vacant and occupied parking spaces, built on a parking lot of The data acquisition system, coined the mobile human presence detection (HPDmobile) system, was deployed in six homes for a minimum duration of one month each, and captured all modalities from at least four different locations concurrently inside each home. The sensor was supposed to report distance of the nearest object up to 4m. The actual range it can report, however, is subject to an internal mode selection and is heavily impacted by ambient light levels. 2022-12-10 18:11:50.0, Euro NCAP announced that starting in 2022, it will start scoring child presence detection, a feature that detects that a child is left alone in a car and alerts the owner or emergency services to avoid death from heat stroke.. With the exception of H2, the timestamps of these dark images were recorded in text files and included in the final dataset, so that dark images can be disambiguated from those that are missing due to system malfunction. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Each sensor hub is connected to an on-site server through a wireless router, all of which are located inside the home being monitored. Learn more. Gao, G. & Whitehouse, K. The self-programming thermostat: Optimizing setback schedules based on home occupancy patterns. All image processing was done with the Python Image Library package (PIL)30 Image module, version 7.2.0. This meant that a Human Subject Research (HSR) plan was in place before any data taking began, and ensured that strict protocols were followed regarding both collection of the data and usage of it. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 7c,where a vacant image was labeled by the algorithm as occupied at the cut-off threshold specified in Table5. Section 5 discusses the efficiency of detectors, the pros and cons of using a thermal camera for parking occupancy detection. In terms of device, binocular cameras of RGB and infrared channels were applied. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. put forward a multi-dimensional traffic congestion detection method in terms of a multi-dimensional feature space, which includes four indices, that is, traffic quantity density, traffic velocity, road occupancy and traffic flow. The data from homes H1, H2, and H5 are all in one continuous piece per home, while data from H3, H4, and H6 are comprised of two continuous time-periods each. Thus new pixel values are generated from linear combinations of the original values. To ensure accuracy, ground truth occupancy was collected in two manners. False positive cases, (i.e., when the classifier thinks someone is in the image but the ground truth says the home is vacant) may represent a mislabeled point. Terms Privacy 2021 Datatang. If nothing happens, download Xcode and try again. (c) Average pixel brightness: 32. Wang F, et al. The batteries also help enable the set-up of the system, as placement of sensor hubs can be determined by monitoring the camera output before power-cords are connected. Environmental data are stored in CSV files, with one days readings from a single hub in each CSV. Multi-race Driver Behavior Collection Data. WebDatasets, depth data, human detection, occupancy estimation ACM Reference Format: Fabricio Flores, Sirajum Munir, Matias Quintana, Anand Krishnan Prakash, and Mario Bergs. 7a,b, which were labeled as vacant at the thresholds used. Dataset: Occupancy Detection, Tracking, and Esti-mation Using a Vertically Mounted Depth Sensor. WebIndoor occupancy detection is extensively used in various applications, such as energy consumption control, surveillance systems, and disaster management. 1a for a diagram of the hardware and network connections. Days refers to the number of days of data that were released from the home, while % Occ refers to the percentage of time the home was occupied by at least one person (for the days released). Points show the mean prediction accuracy of the algorithm on a roughly balanced set of labeled images from each home, while the error bars give the standard deviations of all observations for the home. This is most likely due to the relative homogeneity of the test subjects, and the fact that many were graduate students with atypical schedules, at least one of whom worked from home exclusively. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Rice yield is closely related to the number and proportional area of rice panicles. At present, from the technical perspective, the current industry mainly uses cameras, millimeter-wave radars, and pressure sensors to monitor passengers. Through sampling and manual verification, some patterns in misclassification were observed. Note that the term server in this context refers to the SBC (sensor hub), and not the the on-site server mentioned above, which runs the VMs. Some homes had higher instances of false positives involving pets (see Fig. Data for each home consists of audio, images, environmental modalities, and ground truth occupancy information, as well as lists of the dark images not included in the dataset. The illuminance sensor uses a broadband photodiode and infrared photodiode, and performs on-board conversion of the analog signal to a digital signal, meant to approximate the human eye response to the light level. There was a problem preparing your codespace, please try again. The collecting scenes of this dataset include indoor scenes and outdoor scenes (natural scenery, street view, square, etc.). Test subjects were recruited from the testing universitys department of architectural engineering graduate students and faculty in the front range of Colorado. Timestamp format is consistent across all data-types and is given in YY-MM-DD HH:MM:SS format with 24-hour time. OMS generally uses camera equipment to realize the perception of passengers through AI algorithms. Timestamp data are omitted from this study in order to maintain the model's time independence. Additional IRB approval was sought and granted for public release of the dataset after the processing methods were finalized. The fact that all homes had cameras facing the main entrance of the home made it simple to correct these cases after they were identified. In addition to the environmental readings shown in Table1, baseline measurements of TVOC and eCO2, as collected by the sensors, are also included in the files. Residential energy consumption survey (RECS). Each HPDmobile data acquisition system consists of: The sensor hubs run a Linux based operating system and serve to collect and temporarily store individual sensor readings. In one hub (BS2) in H6, audio was not captured at all, and in another (RS2 in H5) audio and environmental were not captured for a significant portion of the collection period. Other studies show that by including occupancy information in model predictive control strategies, residential energy use could be reduced by 1339%6,7. Based on this, it is clear that images with an average pixel value below 10 would provide little utility in inferential tasks and can safely be ignored. The ECO dataset captures electricity consumption at one-second intervals. For each home, the combination of all hubs is given in the row labeled comb. Since the subsets of labeled images were randomly sampled, a variety of lighting scenarios were present. (c) Waveform after full wave rectification. S.Y.T. Occupancy detection, tracking, and estimation has a wide range of applications including improving building energy efficiency, safety, and security of the occupants. The ANN model's performance was evaluated using accuracy, f1-score, precision, and recall. The Previous: Using AI-powered Robots To Help At Winter Olympics 2022. The publicly available dataset includes: grayscale images at 32-by-32 pixels, captured every second; audio files, which have undergone processing to remove personally If nothing happens, download GitHub Desktop and try again. The code base that was developed for data collection with the HPDmobile system utilizes a standard client-server model, whereby the sensor hub is the server and the VM is the client. Reliability of the environmental data collection rate (system performance) was fairly good, with higher than 95% capture rate for most modalities. It is now read-only. The occupancy logs for all residents and guests were combined in order to generate a binary occupied/unoccupied status for the whole-house. The model integrates traffic density, traffic velocity and duration of instantaneous congestion. The temperature and humidity sensor had more dropped points than the other environmental modalities, and the capture rate for this sensor was around 90%. Install all the packages dependencies before trying to train and test the models. 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